Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models (2501.05382v1)

Abstract: This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as LLMs - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability by testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.

• The paper introduces a roadmap for developing Large Physics Models that merge LLM capabilities with physics-specific symbolic reasoning and data analysis.
• It presents a collaborative framework to boost model accuracy and interpretability through targeted development and rigorous evaluation protocols.
• The study emphasizes interdisciplinary partnerships and philosophical reflection to ensure ethical, robust applications in advanced physics research.

Overview of Large Physics Models: A Collaborative Approach with LLMs and Foundation Models

The paper presents a comprehensive roadmap aimed at guiding the development and evaluation of physics-specific large-scale AI models, termed Large Physics Models (LPMs). These models leverage the capabilities of foundation models, such as LLMs, to address the intricate demands of physics research. The structure of LPMs is illustrated as a framework that enables them to function either autonomously or in conjunction with specialized tools. This includes symbolic reasoning modules for mathematical tasks, data analysis frameworks for experiments, and mechanisms to synthesize insights from theories and literature.

The paper is methodically organized into several distinct sections that outline the potential, design, and deployment strategies for LPMs:

1. Rationale for Dedicated Models: The paper initiates the discussion by addressing whether the physics community should invest in developing physics-specific AI models, as opposed to relying on commercial LLMs. It emphasizes that the unique requirements and complexities of physics research such as precise mathematical manipulation and domain-specific knowledge make a compelling case for customized AI models. These bespoke models can offer higher accuracy, interpretability, and trustworthiness by being attuned to the intrinsic methodologies and standards of physics.
2. Conceptual Framework and Pillars: Three central pillars delineate the roadmap for LPMs: Development, Evaluation, and Philosophical Reflection.
   • Development: This emphasizes constructing models with the capacity to process diverse physics texts, mathematical formulas, and physical data sets. Domain-specific foundation models tailored for subfields such as particle physics, astrophysics, and condensed matter physics are proposed.
   • Evaluation: This focuses on devising benchmarks and evaluation protocols to assess the models' accuracy, reliability, and utility in real-world research scenarios.
   • Philosophical Reflection: This pillar explores the profound implications of AI in scientific discovery, questioning the nature and scope of understanding within these models.
3. Development Challenges and Methods: Various challenges are discussed, including data curation, symbolic reasoning, integration with external resources, and leveraging high-performance computing infrastructure. The paper suggests collaborative endeavors with domain experts and proposes advanced methodologies for model pretraining and finetuning to address these challenges.
4. Evaluation Methodologies: Evaluation pillar details the necessity of constructing physics-specific benchmarks that test core physics knowledge, covering a spectrum from basic to advanced research-level problems. The paper also highlights the importance of understanding the models' robustness, calibration, and ability to generalize beyond their training datasets.
5. Philosophical Implications: This section tackles the epistemological questions surrounding AI systems potentially possessing scientific understanding. It stresses the importance of examining how LPMs might transform scientific practices and understanding and how they align with the societal and ethical values of the scientific community.

In conclusion, the paper underscores its recommendation for the physics community to actively pursue the development of LPMs while proposing the potential for these models to serve as prototypes in other scientific disciplines. This move aligns with maintaining autonomy from commercially-driven AI solutions and ensuring adherence to scientific standards. Future developments would benefit from robust interdisciplinary collaborations and strategic partnerships to mobilize the necessary resources and expertise to advance LPM capabilities.